A deep learning approach to increase the value of satellite data for PM_2.5 monitoring in China

Abstract. Limitations in the current capability of monitoring PM_2.5 adversely impact air quality management and health risk assessment of PM_2.5 exposure. Commonly, ground-based monitoring networks are established to measure the PM_2.5 concentrations in highly populated regions and protected areas such as national parks, yet large gaps exist in spatial coverage. Satellite-derived aerosol optical properties serve to complement the missing spatial information of ground-based monitoring networks. However, such attempts are hampered under cloudy/hazy conditions or during nighttime. Here we strive to overcome the long-standing restriction that surface PM_2.5 cannot be constrained with satellite remote sensing under cloudy/hazy conditions or during nighttime. We introduce a deep spatiotemporal neural network (ST-NN) and demonstrate that it can artfully fill these observational gaps. We use sensitivity analysis and visualization technology to open the neural network black box data model, and quantitatively discuss the potential impact of the input data on the target variables. This technique provides ground-level PM_2.5 concentrations with high spatial resolution (0.01°) and 24-hour temporal coverage. Better constrained spatiotemporal distributions of PM_2.5 concentrations will help improve health effects studies, atmospheric emission estimates, and predictions of air quality.